1. Field of the Invention
The present invention relates to ultra-wideband radar technology, and more specifically, it relates to wireless ultra-wideband (UWB) sensors and networks.
2. Description of Related Art
Wireless distributed systems are being developed to reliably monitor a wide range of environments for both military and civilian applications. Detection of motion can be extremely useful for border management, infrastructure protection, and network-centric warfare applications. Currently there are very few perimeter security systems that utilize a network of interconnected, independent sensors. Most perimeter intrusion detection networks require an extensive infrastructure to be setup in advance. Some of the most modern systems utilize lengths of buried cable placed at a set depth around the entire perimeter to be monitored. Others employ a series of cameras or other motion sensors. All of these options require a substantial infrastructure either for the sensors themselves, or for power and data communications. Also, the majority of current systems are not able to determine where along the monitored perimeter the intrusion occurred. In addition the current technology does not permit a reliable method of target discrimination. Some systems estimate the approximate size of the intruder and try to perform simply discrimination using that information.
The ability to identify targets by ultra wideband radar based on emitted impulse and step frequency wave systems has been investigated previously by many independent researchers. These efforts fall mainly into the following signal processing methods:
The Wigner distribution (WD) combines the autocorrelation function and the Fourier transform to represent signals in a combined time and frequency domain. This technique is very useful for classifying signals backscattered from targets. Researchers have surmised that dolphins use WD's in a likewise manner to classify underwater targets. (Gaunaurd, et al., 1996) (Stifors, et al., 2000).
The wavelet transform is a time-frequency technique that is useful for observing nonstationary properties of ultra-wideband radar echoes. The ability to locate large bandwidth events with high temporal resolution and stationary events with good frequency resolution is attractive in identifying the impulse response of signals and systems such as those found in the UWB target response. Wavelet theory allows the time and frequency resolution tradeoff to be equal at all points. Localization and classification of the singularities in the target impulse response was found to be the best approach for detecting and identifying signals (Chambers, et al., 1993). Discontinuities of interest associated with UWB radar arise from the reflections from different surfaces producing impulsive reflections and their derivatives. Detection of these features would provide indication about the target size, orientation, and shape. Advantages: (1) band limited and time limited events can be localized on a resolution grid approaching the optimal limit, (2) following the transition of the wavelet maximal decay across a range of scales allows the characterization of local discontinuities—a significant aid to target measurement, and (3) the analysis using wavelet transformation corresponds closely to the physical processes, providing a more appropriate framework in which to view dispersive propagation than traditional methods. (Jouny, et al., 1992, 1994) (Fargues, et al., 1993)
The extinction-pulse (E-pulse) discrimination scheme, based upon the singularity expansion method, is commonly used in ultra-wideband radar target discrimination. This technique can be used if the scattering target produces a significant natural response in the frequency range of the radar. The electric field scattered by the target can then be divided into an early-time and a late-time response. The beginning of the late-time response is defined as the maximum amount of time required for the last scattered radiation to travel from the target to the receiving antenna. Early-time corresponds to the forced response period when the excitation waveform is traversing the target, producing a response dominated by localized specular reflections from target scattering centers. The late-time response is the free oscillation period that exists after the excitation waveform has passed.
While the early-time response is very complex, the late-time response can be decomposed into a finite sum of damped sinusoids oscillating at natural frequencies determined by the target geometry. An E-pulse is a transient, finite duration waveform designed to cancel out specific natural resonances of the target, which appear in the late-time response. Thus, when convolving with the late-time target response, the E-pulse can be used to discriminate between targets having different natural frequency characteristics. The correct E-pulse is identified from a predefined library of E-pulses by the convolution with the lowest energy.
The late-time E-pulse technique is aspect independent. Since the target resonance frequencies are independent of the excitation waveform, the late-time E-pulse technique will function regardless of the aspect angle of the incident waveform on the target A disadvantage of using the late-time E-pulse technique is that when the target resonant response is available, it often has low signal strength. Researchers have demonstrated this technique to work reasonably well in noisy environments; however, if the SNR is too low, ambiguous results are obtained (Mooney, et al., 2000).
A more complex method using the higher signal strength early-time response may be desirable. The early-time technique cancels the frequency domain sinusoidal functions arising from aspect dependent temporal positions of the specular reflections. The early-time technique can yield more explicit results; however, E-pulses must be defined for the target in each different aspect angle. For optimal target identification, researchers have developed methods that combine the E-pulse time domain analysis for the late-time response, and frequency domain cancellation in the early-time response (Rothwell, et al., 1994).
Several researchers continue to study, develop and improve the E-pulse methods. (Rothwell, et al., 1985, 1986, 1995 Chen, et al., 1986, 1994 Ross, et al., 1990, 1994, 1998, Li, et al., 1998, Damjanschitz, et al., 1999) The aforementioned methods consider only single targets. In the case of multiple targets, one must consider the effects of mutual coupling and natural frequency differences of the system as compared to isolated targets. A drawback of conventional E-pulse analysis is that it requires a priori knowledge about the natural frequencies of the target for late-time analysis, and the locations and transfer functions of target scattering centers as a function of aspect angle for early-time analysis. Developments are being made to enable the extraction of E-pulses directly, without a priori knowledge of their response. In addition, means of eliminating redundancies and reducing computing demands from the combined early-time/late-time method have been recently published.
Techniques using third order statistics can reveal information about non-Gaussian signals and nonlinearities, which cannot be observed using conventional, second order techniques. Higher Order Spectra (HOS) techniques are frequently used in speech processing and have been used with ultra-wideband radar to classify targets by extracting bispectral signatures related to geometric and textural properties of the target (Marmarelis, et al., 1992).
Kernal analysis is an HOS technique that involves treating the target as a black-box system with an unknown impulse response. Experimental data consisting of incident waveforms and return signals for various targets are used to develop models of varying order to describe and classify the impulse response. The backscattered echoes from an unknown target and the incident impulse are then used to determine the impulse response model with the best fit.
One method of obtaining an approximate temporal impulse response of a target is from frequency domain physical optics (FDPO). This technique is accurate only at frequencies where the wavelength is very short compared to the target, and for angles that don't induce traveling waves. Time domain physical optics (TDPO) may be more appropriate for developing response models to large faceted targets because it can more efficiently analyze a large amount of scattering data (Skinner, et al., 1993). The greatest error in these techniques is in modeling targets that are curved in the direction of propagation, due to non-linear progression of the surface's current phase along the body. Skinner also reports that knowledge of the facetization of the target is required for result accuracy.
Previous work (Abrahamasson, et al., 1991) predicts the form-function in the backscattering radar cross-section of spherical dielectric targets using a discrete Fourier transform (DFI) technique. Target identification is performed using target resonances extracted using echoes from scattering structures. Another computational method (Skinner, et al., 1993) called FDPO reports that knowledge of the facetization of the target is required for result accuracy. More recent research (Mooney, et al., 2000) uses energy discrimination number (EDN) statistics for E-pulse target identification. This work developed a theoretical method for determining the probability of identifying a target from a family of M targets.
The above findings add significantly to the body of knowledge for discrimination methods using received energy from target reflections, however, such techniques generally employ higher power that results in significantly stronger reflected energy.
Accordingly, it is desirable to have available a low power, deployable sensor network having the ability to be rapidly deployed with minimal infrastructure required. It is desirable that such a system could actively detect, discriminate, and track targets as they passed through the perimeter. The present invention provides such devices and their methods of operation.